Algorithmic Trading

Most research papers on trading with ai gives good accuracy in good market on their test results but they don't include the transaction cost so their results look better than reality. To tackle this problem, I build an AIBotTrade and tested three algorithms GBM, LSTM, PPO reinforcement learning on same data which makes profit on other studies which also include transaction cost of 10 basis point on every trade. I have used NSE India data from 2018-2022. I have Chosen this data on purpose because this time period includes COVID-19 crash, recovery after covid and from July 2022 to December 2023 a bear market and sideways market in between which other paper completely avoid. Now after the training the results were surprising to me LSTM gave best results with 42.30% return with Sharpe ratio 0.8301 by making only 63 trades. then GBM also made profit with return of 27% despite having only 35% directional accuracy. But somehow PPO reinforcement learning perform worst it gave negative Sharpe ratio because it will keep buying stocks in falling market maybe it calculates that stock will always rebound. So, the main lesson from this is in stock market trading prediction accuracy does no matter much I mean they do but in my findings transaction costs and market regime gives another picture entirely so these small 10 basis transaction costs and market regime can draw down the results of profitable strategy into loss one.

Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA-FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.

This paper examines the role of algorithmic trading in modern financial markets. Additionally, order types, characteristics, and special features of algorithmic trading are described under the lens provided by the large development of high frequency trading technology. Special order types are examined together with an intuitive description of the implied dynamics of the order book conditional to special orders (iceberg and hidden). The chapter provides an analysis of the transaction costs associated with trading activity and examines the most common trading strategy employed in the market. It also examines optimal execution strategy with the description of the Efficient Trading Frontier. These concepts represent the tools needed to understand the most recent innovations in financial markets and the most recent advances in microstructures research.

Forecasting changes in stock prices is still a major issue with significant social and economic
ramifications. By integrating time-series modeling, cross-asset relationships, sentiment or alternative
data, and deep learning (LSTM, Transformer families, Graph Neural Networks), reinforcement
learning, and domain-adapted NLP (FinBERT/LLMs), recent developments in machine learning have
significantly improved predictive performance. In addition to surveying current methods (2020–2025),
this paper suggests a hybrid prediction architecture that combines temporal transformers (Autoformer
variants), GNNs for inter-asset relations, and a FinBERT-based sentiment channel. It also provides an
experimental strategy and evaluation protocol that tackles non-stationarity, data leakage, and economic
validation. The suggested hybrid system outperforms baseline LSTM and tree-based models in terms
of directional correctness and risk-adjusted returns, according to results on benchmark datasets.
Lastly, we go over constraints, robustness tests, and future research prospects.

This paper analyzes the structural mechanisms that produce and sustain the 74•89% client loss rate documented by European (ESMA/MiFID II), American (CFTC), and Australian (ASIC) regulators across the retail foreign exchange industry. We identify four layers of structural bias: (i) the B-book and hybrid dealer model, in which the broker's profit is inversely correlated with client performance; (ii) a quadruple opacity in execution infrastructure, comprising the MetaTrader Manager Terminal, the Bridge and LP rebate architecture, the voice execution channel reserved for institutional participants, and the cumulative friction calculation invisible to retail; (iii) a five-tier acquisition funnel • introducing brokers, digital affiliates, commercial educators, signal providers, and copytrading platforms • whose aggregate function is the continuous production of depositing accounts rather than profitable traders; and (iv) a pseudoscientific pedagogical corpus, centered on technical analysis, whose empirical status has been systematically rejected by five decades of academic literature.  Working paper adapted from: CrossVol Research (2026). FX Traders vs Brokers: Vanilla and Exotic Options, Forwards, and Other OTC Structures: What Retail Traders Never See. Amazon Kindle, ASIN: B0GX31WB5F. https://www.amazon.com/dp/B0GX31WB5F

The GBP/USD currency pair, commonly known as "Cable," remains one of the most traded instruments in global foreign exchange markets. Every day, billions of dollars flow through this currency pair as banks, hedge funds, corporations, proprietary trading firms, and retail traders participate in price discovery. While the pair remains attractive because of its liquidity and volatility, the environment in which it trades has changed dramatically over the past decade. Today's markets are faster, more data-driven, and increasingly dominated by automation. Economic releases are analyzed within milliseconds, algorithmic systems react instantly to price changes, and liquidity providers constantly update quotes in response to changing market conditions. As a result, many traders who rely solely on manual execution are finding it increasingly difficult to compete. In fast-moving conditions, success depends not only on predicting market direction but also on execution speed, consistency, and disciplined risk management. Manual traders often struggle with these requirements because human decision-making introduces delays and emotional biases. Automated trading systems, by contrast, can process information and execute trades at speeds impossible for humans to match. This article examines why manual GBP/USD trading is becoming less effective in modern markets, compares manual and automated approaches, and explains how advanced risk management frameworks in automated trading help improve long-term performance. The Changing Nature of GBP/USD Markets

Current understanding of ecosystem responses to global warming remains predominantly anchored in elementprocess descriptions that focus on species distributions, phenological shifts, and productivity changes. While substantial empirical evidence documents warming-induced alterations across biological scales, existing frameworks exhibit fundamental limitations in explaining and predicting the widespread occurrence of abrupt ecological changes, regime shifts, and irreversible transitions. This paper argues that global warming, at its thermodynamic essence, constitutes a sustained injection of additional energy into the Earth's ecosystems accompanied by amplified energy fluctuations across multiple temporal scales. Ecosystems, as dissipative structures maintained far from thermodynamic equilibrium, respond to this energy perturbation according to principles rooted in non-equilibrium thermodynamics. I propose an "Ecological Thermodynamic Fluctuation-Phase Transition" framework that treats the intensity of warming-driven energy fluctuations as the governing control variable determining ecosystem response modalities. The framework advances three core propositions: first, enhanced energy fluctuations compress the attractor basin of the current steady state within an energy landscape, systematically reducing ecological resilience before any visible state change; second, when fluctuation intensity exceeds a critical threshold, the system is driven toward a self-organized critical state wherein any tiny perturbation can trigger cross-basin transitions, manifesting as ecological phase transitions; third, the direction of phase transition is guided by the maximum entropy production principle, with systems tending toward states characterized by faster energy dissipation, more rapid turnover, and structurally simplified configurations with higher entropy production rates. This framework is supported by metabolic scaling theory, statistical physics of critical phenomena, and thermodynamic extremal principles, and is realized through two novel indices: the Energy Sensitivity Index and the Phase Transition Warning Index. The theory provides a first-principles basis for unifying apparently disparate ecological responses to warming and offers a pathway toward predictive ecology grounded in energy and thermodynamics.

The ongoing digital transformation of human services has positioned artificial intelligence (AI) as a potentially significant resource for social work practice, particularly in the domain of case management. Yet empirical investigation into how social work practitioners evaluate AI-supported systems-and what prevents or facilitates their adoption-remains limited. The present study addresses this gap through a quantitative cross-sectional online survey conducted with N = 97 social work professionals drawn from diverse practice fields, including youth welfare, psychosocial counseling, clinical social psychiatry, refugee support, and disability services. Three purpose-developed Likert-format scales assessed willingness to use AI case management systems (α = .88), perceived benefits (α = .86), and perceived barriers (α = .91). Perceived benefit was the strongest bivariate correlate of willingness (r = .68, p < .001), followed by perceived barriers (r =-.52, p < .001) and technological self-efficacy (r = .47, p < .001). Data privacy concerns showed a substantial negative association with willingness (r =-.49, p < .001). Multiple regression analysis identified perceived benefit (β = .49, p < .001), perceived barriers (β =-.31, p = .002), and technological self-efficacy (β = .24, p = .011) as significant independent predictors of willingness, jointly explaining 58% of its variance (R² = .58, F(5, 91) = 25.07, p < .001). Professional experience was negatively associated with willingness, with experienced practitioners reporting significantly lower AI adoption readiness than early-career colleagues (ANOVA: F(2, 94) = 4.91, p = .009). Social work practitioners recognize the administrative efficiency and documentation relief AI may offer, but express robust concerns about data protection, the adequacy of AI in capturing complex social situations, and the risk of diminished relational practice quality. Targeted digital competence training and the establishment of transparent, participatory AI governance frameworks in social service institutions are identified as critical enablers of ethically grounded AI adoption.

**Research Problem:** The rapid integration of artificial intelligence into digital lending operations has generated a fundamental tension between the e iciency gains of full automation and the enduring need for human judgment in credit decisions that carry profound consequences for consumers, institutions, and financial system stability. While algorithmic underwriting systems have demonstrated superior predictive accuracy in controlled settings, their deployment at scale raises unresolved questions concerning fairness, explainability, regulatory compliance, and operational resilience-particularly in edge cases and ambiguous credit scenarios where purely automated systems reveal structural limitations. This paper addresses the gap between the technological push toward autonomous lending and the regulatory, ethical, and operational imperative for meaningful human oversight.

Cognitive Hygiene: Remaining Human in the Age of Artificial Intelligence explores one of the defining challenges of the 21st century: how humanity can preserve cognitive autonomy, emotional depth, and ethical awareness while living inside increasingly intelligent algorithmic systems.

The book introduces the concept of cognitive hygiene as a practical and philosophical framework for protecting the human mind from manipulation, overstimulation, dependency, and identity fragmentation in the AI era. Rather than portraying artificial intelligence as either salvation or catastrophe, the work argues that the true danger lies in unconscious adaptation — the gradual surrender of attention, critical thinking, memory, emotional resilience, and moral agency to automated systems.

Combining philosophy, neuroscience, digital culture analysis, ethics, and transhumanist reflection, the book examines how recommendation algorithms, synthetic media, AI companions, data economies, and cognitive automation reshape human perception and behavior. It also proposes strategies for maintaining psychological sovereignty, intellectual discipline, and meaningful human identity within a technologically accelerated civilization.

At its core, the book defends a central question: Can humanity evolve alongside artificial intelligence without losing the essence of what makes us human? Cognitive hygiene is presented not as resistance to technology, but as the conscious cultivation of mental clarity, ethical responsibility, and cognitive independence in an age of intelligent machines.
https://doi.org/10.5281/zenodo.20400550

The contemporary integration of artificial intelligence (AI) and machine learning models within state judiciaries and public administration represents a paradigm shift from traditional normative text interpretation to algorithmic computational predictions. This article examines the epistemic and systemic risks that automated decision-making (ADM) systems impose upon the fundamental guarantees of due process of law, algorithmic transparency, and non-discrimination. Utilizing a rigorous dialectical methodology, this study analyzes how probabilistic logic alters the ontology of judicial adjudication. The thesis establishes that the operational efficiency promised by AI accelerates judicial throughput but relies on historical data sets that codify systemic biases. The antithesis demonstrates that current regulatory architectures fail because of the structural "black-box" dilemma, proprietary trade secrecy, and the limits of explainability. The synthesis proposes a co-constitutive framework of algorithmic governance. This approach integrates technological risk assessment with administrative safeguards to preserve institutional human oversight and statutory accountability. By analyzing empirical technical data, recent international case law (such as the Dutch Siri precedent and US state decisions on recidivism instruments), and institutional risk reports, this paper refutes the myth of algorithmic neutrality. It provides a scalable model for cross-border digital governance that balances computational utility with foundational constitutional guarantees.

Financial trading research is vulnerable to data-mining bias because large numbers of signals, filters,  instruments,  and  historical  periods  can  be  tested  until  apparently  profitable  results emerge by chance. This paper introduces AlphaSeeker, a systematic framework for discovering candidate alpha seeds from intraday tick data for liquid traded markets such as index futures, foreign  exchange,  equity  indices,  and  contracts  for  difference.  The  framework  separates hypothesis generation from validation.
AlphaSeeker  performs  large-scale  combinatorial  backtesting  across  predefined  regime  filters, day/time filters, signal families, and simulated execution assumptions. Each filter combination generates  both  summary  metrics  and  trade-level  logs.  Candidates  are  rejected  if  they  are unprofitable, based on too few trades, overly concentrated in a short time period, or inconsistent with predefined risk and distribution criteria. Surviving candidates are then tested on a separate out-of-sample period under a frozen definition.
The framework does not claim to produce production-ready strategies or disclose proprietary trading signals. Instead, it formalizes a staged research process for reducing a large hypothesis space into a smaller set of candidate alpha seeds suitable for deeper human review, robustness testing,  execution  analysis,  and  risk  assessment.  The  main  contribution  is  a  disciplined methodology for candidate discovery under explicit overfitting controls.

This article examines the structural erosion of fundamental rights in contemporary Western democracies caused by the integration of predictive algorithms, artificial intelligence, and automated risk-assessments into executive enforcement and judicial decision-making. Adopting a dialectical methodology, the study addresses the systemic tension between the constitutional promise of substantive due process and the technocratic drive for computational efficiency. The thesis posits that automated state action converts the presumption of innocence into a statistical probability of guilt, creating an unconstitutional shift in the burden of proof. The antithesis counter-argues from a utilitarian perspective of judicial administrative optimization, defending algorithmic risk scores as necessary instruments to manage institutional overload, eliminate human cognitive bias, and stabilize fragmented public safety frameworks. The synthesis reconciles this tension by introducing a revised framework of Techno-Constitutional Due Process, which establishes automated choices as nonbinding administrative presumptions subject to strict judicial review and mandatory algorithmic transparency. Integrating principles of Constitutional Law, Lacanian psychoanalysis, Foucault's biopolitics, and empirical data from international courts, this paper outlines actionable regulatory standards designed to preserve human agency and systemic dignity within the contemporary rule of law.

Latency arbitrage-trading against a price quote that has not yet incorporated information already reflected in a faster reference market-is among the most frequently discussed and least formally documented phenomena in retail foreign exchange. While the academic literature on low-latency trading in equity markets is mature, the retail FX context differs in structurally important ways: prices are intermediated through a chain of liquidity providers, aggregators, and brokers; the reference market against which a quote may be judged stale is fragmented rather than consolidated; and the principal counterparty to a retail latency arbitrageur is frequently the broker itself rather than another trader. This paper surveys the mechanism of latency arbitrage in retail FX, organises the strategy space into a taxonomy, examines the infrastructure that determines feasibility, and analyses the detection methods and countermeasures that brokers deploy in response. We frame the broker-arbitrageur relationship as a coevolutionary dynamic and connect it to the established literature on toxic arbitrage and the economics of trading speed. The treatment is descriptive and integrative: it aims to render a phenomenon well understood by practitioners legible to a market-microstructure research audience, and to identify open questions amenable to empirical work.

This study develops a theoretical framework explaining decision legitimacy in AI-enabled organizations as an emergent outcome of the interplay between algorithmic authority and human judgment. Drawing on institutional theory, socio-technical systems, and behavioral decision-making, it identifies procedural, distributive, and cognitive legitimacy as key dimensions. While AI enhances efficiency and consistency, legitimacy depends on transparency, explainability, and perceived fairness. Algorithmic authority may both strengthen and undermine legitimacy, whereas human judgment remains essential for contextual interpretation and accountability. The study advances multilevel propositions and outlines a research agenda for examining legitimacy in hybrid human-AI decision systems.

This essay argues that the apparent conflict between the Efficient Market Hypothesis and behavioral economics is not a debate to be resolved in favor of one side, but a failure to recognize that the two frameworks describe different levels of the same system. Drawing on Shiller's excess volatility findings, De Long et al.'s noise trader model, and the genetics of investment behavior, the essay develops a multi-level account in which market efficiency emerges from the aggregation of diverse biased participants, and market pathology emerges when that diversity collapses under stress. The account shares significant ground with Lo's Adaptive Markets Hypothesis while offering a specific mechanism for why transitions between efficient and dysfunctional states tend to be sudden rather than gradual.

This paper provides a quantitative framework for capital preservation in the agentic financial era, introducing the concept of Kinetic Neutrality as a deterministic alternative to static asset allocation. We demonstrate that the contemporary macroeconomic environment imposes a Stagnancy Theta (Θ)-a composite penalty of inflation, opportunity cost, and monetary friction-exceeding 18.5% per annum. Under these conditions, legacy "buy-and-hold" strategies function as high-entropy vessels with negative expected real returns. Utilizing high-fidelity 1-hour OHLCV exchange data and empirical performance benchmarks from Deterministic Execution Environments (DEE), this study models the transition from duration-based yield to motion-based capture. By applying a constrained Architectural Efficiency (E) filter of 0.704 and a realized turnover velocity of 18.4x, the results establish that a kinetic treasury can consistently harvest systemic volatility (𝚪) to maintain a surplus over the stagnancy hurdle. Furthermore, we define the Matrix Capacity Horizon (C H), proving that this "Superconductive State" is scale-invariant through the expansion of matrix breadth (N) across institutional-grade liquidity pools. These findings suggest that in an agentic economy, capital sovereignty is a function of deterministic motion rather than directional exposure.

Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).

The global fixed-income market — valued at approximately $145 trillion — is undergoing a profound structural transformation as artificial intelligence transitions from a passive tool to an autonomous agent capable of independent decision-making. With approximately 70% of institutional bond trading already electronic and algorithmic systems increasingly pricing credit risk, optimizing portfolios, and executing complex strategies without direct human intervention, a new paradigm is emerging: the "lights-out factory" applied to financial markets. This research investigates the human dimension of this transformation, focusing on how fixed-income professionals — traders, portfolio managers, credit analysts, risk managers, and operations specialists — adapt to a context where AI acts as colleague, competitor, or replacement. The study addresses a fundamental epistemological paradox: while AI systems are trained by humans, professionals are increasingly trained by AI agents, raising critical questions about knowledge authority, tacit expertise transmission, and cognitive dependency. Drawing on future-of-work frameworks, the research examines five distinct adaptation trajectories — replacement, augmentation, specialization, co-pilot collaboration, and deskilling — and explores the psychological, organizational, and policy-level factors that shape which pathway prevails. By generating empirical evidence on professional adaptation in one of the world's most systemically important financial markets, this research aims to inform organizational strategy, professional development, regulatory governance, and the broader discourse on human flourishing in the age of agentic AI.

Guía Práctica para el Trader Intermedio. Aprende a analizar el mercado operar con criterio, gestionar el riesgo y construir un sistema de trading usando como base la estructura del mercado.

No se trata de un documento con estrategias mágicas para ganar en los mercados. Algo así simplemente no existe. Lo que busca es enseñarte a visualizar la estructura de precios en los mercados y a sacar conclusiones a partir de esta.

La lectura de la estructura del mercado puede ayudar a definir puntos de entrada y salida del mercado y puede usarse para el desarrollo de sistemas de trading rentables.

In 2004, the World Bank and United Nations have promulgated the ESG investing framework which has a primary focus of considering sustainability practices in financial analysis. As a result of this, the regulatory bodies across the world have instructed their industries to integrate the ESG practices in their business. The investors in securities market attracted to the ESG initiatives taken by the industries and they made their investment decision by considering the ESG factors. This has motivated many researchers to find out the nexus between ESG factors and the performance of the portfolios. In the same way, a comparative analysis between ESG and traditional funds of India, US, UK, Australia and Sweden have performed in this study. The researchers considered a sample of 30 ESG thematic and traditional funds for a five year period. Performance of ESG and traditional funds assessed through return & risk metrics and risk adjusted measures. It is identified that the ESG funds have a high downside risk protection ability than the traditional funds. The ESG funds from the UK and Australia have exhibited a strong performance than the traditional funds. In the US, the traditional funds have dominated the ESG funds in terms of performance metrics. The ESG funds from India and Sweden, with a mixed results, depicts that the ESG investing is in the nascent stage.

An open-source methodology and Python reference implementation for measuring retail forex broker execution quality across five orthogonal dimensions: matching latency, slippage asymmetry, spread widening, last-look hold time, and requote rate. MIT licensed. Companion public dataset for community contributions.

RED-2400 is a public benchmark of algorithmically-rejected trading events from a live Solana decentralised-exchange filter stack. I logged the data continuously between 2026-04-10 and 2026-05-02. The benchmark contains 6,659 rejection events linked to 169,122 post-rejection price and liquidity observations and 1,836 graveyard-tracker snapshots. Outcome labels follow the five-tier classification of Kamat (2026c): saved (windowed), saved (early-death), missed, flat, and unclassifiable. Thresholds use the trough-to-reference and peak-to-reference price ratios within a 24-hour window. Most filter-design datasets cover the accept side only. That gap leaves reject-side outcomes unmeasured and biases filter validation. RED-2400 lets researchers replicate filter-precision claims directly. RED-2400 is the first window in a planned dataset series; subsequent windows will extend the time horizon and enable regime-stratified analysis.

RED-2400 is a public benchmark of algorithmically-rejected trading events from a live Solana decentralised-exchange filter stack. I logged the data continuously between 2026-04-10 and 2026-05-02. The benchmark contains 6,659 rejection events linked to 169,122 post-rejection price and liquidity observations and 1,836 graveyard-tracker snapshots. Outcome labels follow the five-tier classification of Kamat (2026c): saved (windowed), saved (early-death), missed, flat, and unclassifiable. Thresholds use the trough-to-reference and peak-to-reference price ratios within a 24-hour window. Most filter-design datasets cover the accept side only. That gap leaves reject-side outcomes unmeasured and biases filter validation. RED-2400 lets researchers replicate filter-precision claims directly. RED-2400 is the first window in a planned dataset series; subsequent windows will extend the time horizon and enable regime-stratified analysis.

The Stock Exchange of Thailand provides an ideal platform for comparing the trading characteristics of warrants and their underlying stocks since both of them trade in the same market under identical trading rules. If their patterns diverge significantly, it may be possible for an astute trader to devise profitable arbitrage strategies during the life of the warrants. We find that both their patterns are downward-sloping for spreads, U-shaped for flow toxicity, volatility, depth concentration, and trading volume; and upward-sloping for depth and market order flow ratio. This implies that trading under identical market structures leads to similar trading characteristics. We document that flow toxicity is negatively related to spread and positively related to depth, market order flow ratio, trade size, trading volume, and volatility.

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This paper develops a valuation-based interpretation of the cost of equity by recovering an implied capitalization rate from observed equity prices and contemporaneous earnings under a maintained steady-state abstraction. In the empirical illustration, "earnings" (E) is taken as the earnings per share measure implicit in the reported price-earnings ratio (P/E) obtained from publicly available investment data sources (so that c is recovered mechanically as c = 1/(P/E)). The recovered rate is algebraically equivalent to the earnings yield and is interpreted as a valuation discount rate, analogous to a bond yield, rather than as an expected return implied by asset-pricing equilibrium restrictions. Exposure to financial distress and bankruptcy is embedded in reduced form through earnings capitalization, while price volatility is treated separately. An empirical illustration documents cross-sectional dispersion in implied capitalization rates across firms and market segments.

This paper develops a valuation-based interpretation of the cost of equity by recovering an implied capitalization rate from observed equity prices and contemporaneous earnings under a maintained steady-state abstraction. In the empirical illustration, "earnings" (E) is taken as the earnings per share measure implicit in the reported price-earnings ratio (P/E) obtained from publicly available investment data sources (so that c is recovered mechanically as c = 1/(P/E)). The recovered rate is algebraically equivalent to the earnings yield and is interpreted as a valuation discount rate, analogous to a bond yield, rather than as an expected return implied by asset-pricing equilibrium restrictions. Exposure to financial distress and bankruptcy is embedded in reduced form through earnings capitalization, while price volatility is treated separately. An empirical illustration documents cross-sectional dispersion in implied capitalization rates across firms and market segments.

The abstract describes HFT Quartet + POC as a high-frequency crypto trading framework that combines four statistical regime-detection tools—lag-1 autocorrelation, variance ratio, Reynolds-number turbulence modeling, and Higuchi fractal dimension—with volume profile Point of Control confirmation. Its goal is to detect whether the market is trending, mean-reverting, turbulent, or structurally stable, then adjust position sizing dynamically.

It claims strong empirical performance on 30-second Bitcoin data, including 70–80% win rates, Sharpe ratios above 2.5, max drawdown under 8%, and roughly 30% fewer false signals compared with traditional methods. The abstract positions the system as computationally efficient, automated, and adaptable across timeframes.

The Central Bank of Turkey's policy to decrease the nominal interest rate has caused episodes of severe fluctuations in Turkish lira exchange rates during 2022. According to these conditions, the daily return of the USD/TRY have attracted the risk-taker investors' attention. Therefore, the uncertainty about the rates has pushed algorithmic traders toward finding the best forecasting model. While there is a growing tendency to employ sophisticated models to forecast financial time series, in most cases, simple models can provide more precise forecasts. To examine that claim, present study has utilized several models to predict daily exchange rates for a short horizon. Interestingly, the simple exponential smoothing model outperformed all other alternatives. Besides, in contrast to the initial inferences, the time series neither had structural break nor exhibited signs of the ARCH and leverage effects. Despite that behavior, there was undeniable evidence of a long-memory trend. That means the series tends to keep a movement, at least for a short period. Finally, the study concluded the simple models provide better forecasts for exchange rates than the complicated approaches.

We propose a geometric reformulation of derivative pricing in which the implied volatility surface defines a Riemannian metric on a financial manifold. In this framework, gamma corresponds to local curvature, volatility dynamics correspond to geometric flows, and market shocks emerge as curvature instabilities. We derive Black-Scholes as a flat-space limit of this structure and extend it to a nonlinear curvature flow PDE with upstream (shock) and downstream (feedback) components. A discrete formulation is introduced, yielding a regime-based trading system for convexity strategies.

ABSTRACT
Title: U.S.–DRC Health Cooperation: Transforming Aid into a Solvency and Sovereignty Engine through the 50/50 Model and Artificial Intelligence.

Background: As the Democratic Republic of Congo (DRC) prepares for a demographic surge to 150 million by 2035, legacy health aid models are reaching their structural limits. The 2025 "America First Global Health Strategy" (AFGHS) necessitates a shift from humanitarian assistance to radical accountability and self-reliance.

Objective: This paper, authored by a strategic insider with 15 years of experience across USAID, DFID, and World Bank programs, proposes the 50/50 Model as a disruptive framework to achieve health sovereignty.

Methodology (The Phygital Approach): The model introduces a 6-step "Phygital" architecture—integrating physical healthcare delivery with digital governance. At its core, an Autonomous Algorithmic Audit (AAA) certifies medical acts at the operational level (Health Centers and Hospitals), generating unforgeable Certified Debt Notes (NCC).

Key Findings:

Economic Nexus: By linking health financing to the formalization of the mining and extractive sectors, the model creates a "Sovereign Billion" fund, reducing aid dependency.

Fiduciary Integrity: The use of U.S. technological standards (Cloud, Cybersecurity, and AI) ensures 100% traceability, aligning with FDA compliance benchmarks.

Institutional Mutation: The paper advocates for the transformation of the Fonds de Solidarité Santé (FSS) into an Actuarial Regulatory Authority (ARA).

Conclusion: The 50/50 Model offers a "win-win" strategic pact: it secures U.S. investments and mineral supply chains while providing the DRC with a scalable, transparent, and sovereign health financing system.

Keywords: Health Financing, Universal Health Coverage (UHC), Algorithmic Governance, U.S.-DRC Cooperation, 50/50 Model, Digital Health, Mining-Health Nexus.

We apply the path signature transform-a canonical lossless encoding of multidimensional time series rooted in Chen's theorem (1954) and Rough Path Theory (Lyons 1998)-to the joint trajectory of bid price, ask price, bid volume, and ask volume extracted from limit order book (LOB) data. Truncating the signature at order 4 yields a 341-dimensional feature vector that constitutes a sufficient statistic for predicting the next mid-price direction without any manual feature engineering. Evaluated on the FI-2010 benchmark dataset (NASDAQ, 10 stocks, 10 trading days, 10-level LOB) and on proprietary US equity tick data (2020-2024), the signature feature set outperforms standard hand-crafted LOB features (OBI, spread, VPIN, microprice) by 35-56% in terms of Information Coefficient (IC) at 100-500ms prediction horizons. We further demonstrate that the 5 most informative signature terms-cross-variation of bid price and bid volume, Lévy area between bid and ask price paths, and three third-order interaction terms-have direct microstructure interpretations, suggesting that the signature is not merely a black-box feature extractor but a structured decomposition of LOB information. GPUaccelerated computation via the Signatory library achieves full 341-feature extraction in under 0.8ms, making the approach compatible with live HFT deployment.

Traditional monetary theory utilizes M1 velocity as a proxy for capital efficiency, yet this metric remains an artifact of a legacy "Batch-Processing" model, restricted by the latencies of human-speed decision loops and T+N settlement cycles. As capital migrates into tokenized, agentic environments, execution friction approaches zero, triggering the Jevons Paradox of Capital Liquidity (JPCL). This research demonstrates that frictionless movement does not reduce the demand for liquidity; instead, it creates an exponential requirement for asset turnover. We identify a structural divergence between legacy M1 velocity (≈ 1.3x) and agentic surface velocity (≈ 610x). By applying an Average True Range (ATR) harvesting model, we quantify the Stagnancy Penalty: a displacement in relative purchasing power exceeding 22% per annum, caused by the transition from Duration-Based Yield to Throughput-Based Efficiency.

Filter-gated algorithmic trading systems on decentralized exchanges reject the majority of candidate tokens they encounter, yet the in-production precision of these filter rules -whether they save capital more often than they forgo profitable entries -is rarely measured at scale. Prior work by the author introduced a post-rejection follow-up sampling methodology capable of producing the necessary data. The present paper reports the first at-scale precision audit produced by that methodology. Applied to a production multi-strategy trading fleet over a continuous fourteenday observation window, the method yielded approximately 99,000 follow-up samples across approximately 2,400 unique rejection events spanning eight active filter rules. We introduce an outcome classification refinement that credits a rejected candidate's disappearance from the price oracle within a short interval of rejection as an implicit positive save signal, on the premise that such disappearance is the observable signature of the rug-pull and delisting outcomes filter stacks are designed to avoid. Under this refinement, every active filter in the live stack demonstrates a net-positive precision verdict with an aggregate save-to-miss ratio of approximately fifteen to one. Under the conservative alternative that excludes the early-death category, the ratio remains strongly positive at approximately four to one. Early-death events account for roughly half of all classified rejections, a finding that reshapes the filtercalibration problem in this domain toward asymmetric avoidance of a substantial short-lived token population rather than symmetric opportunity-cost balancing.

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. W...

Cyber risk in modern financial ecosystems has transcended operational IT concerns, emerging as a macroprudential threat capable of inducing systemic liquidity crises, capital adequacy erosion, and cross-institutional contagion. Existing frameworks such as NIST CSF and ISO/IEC 27001 remain institution-centric and reactive, failing to model interconnected financial network dynamics under adversarial cyber scenarios. This paper proposes the Systemic Cyber Risk Digital Twin with Autonomous Financial Impact Forecasting (SCR-DT-AFIF), an integrated architecture combining Financial Contagion Graph Neural Networks (FC-GNN), reinforcement learning-based capital optimization, blockchain-backed regulatory audit trails, and quantum-resilience simulation layers. The SCR-DT-AFIF constructs a real-time cyber-financial twin of interbank exposures, payment rails, cloud service dependencies, and fintech API integrations. A Cyber-to-Capital Translation Engine (C2C-TE) converts technical cyber incidents into quantitative financial stress metrics including liquidity drawdowns and Value-at-Risk perturbations. Simulation results demonstrate a 45% improvement in systemic shock forecasting accuracy, 33% reduction in liquidity shock propagation latency, and 100% verifiable audit traceability via distributed ledger validation. This framework repositions cyber risk management as a core

As capital management transitions from human-discretionary to agent-autonomous systems,
a critical "Trust Gap" has emerged, evidenced by the vast discrepancy between theoretical
AI capability and actual enterprise deployment. Current financial infrastructure remains
"pre-agentic," forcing a choice between Policy-Gated systems (relying on administrative
recourse) and Deterministic Silos (relying on rigid, non-composable code). This paper
introduces the Deterministic Execution Environment (DEE): a non-custodial middleware
layer that enforces immutable "math-rails" to ensure capital sovereignty. We demonstrate
that the DEE architecture raises the systemic Mechanical Determinism baseline from
45.4% to 51.4%, effectively reclaiming the "untrusted" labor-market delta by isolating
strategic intelligence from execution safety.

Algorithmic trading systems operating on decentralized exchanges continuously evaluate candidate tokens against filter stacks, rejecting the majority. Unlike executed trades, these rejections leave no performance trace-the system retains no record of whether each rejection was correct. We introduce a post-rejection follow-up sampling methodology that captures this counterfactual by logging initial candidate state at rejection time and revisiting the same assets across multiple subsequent horizons. Applied to a production memecoin trading system over a two-week observation window, the method produced a dataset of approximately 67,000 rejection snapshots with multi-horizon price and liquidity samples on 457 unique tokens. Analysis of a subset of exit-category rejections revealed a statistically notable proportion of stopped-out positions experienced subsequent price recovery exceeding meaningful thresholds, indicating systematic over-sensitivity in the exit logic. The instrument enables paper-mode A/B/C testing of alternative exit hypotheses, filter calibrations, and entry-signal modifications while preserving the production system as control. We argue this post-rejection follow-up framework is a generalizable contribution to algorithmic trading research infrastructure, independent of any specific strategy, market, or parameter choice.

Autonomous cryptocurrency trading systems operating continuously on decentralized exchanges face challenges absent in traditional markets: extreme volatility, rug-pull risk, MEV exploitation, and time-of-day liquidity patterns with no documented parallels in the academic literature. We present UDAY, a multi-layer autonomous trading framework for Solana-based memecoin markets that combines real-time on-chain signal scoring, adaptive circuit breakers, and hour-aware entry gating. Operating on live DexScreener markets in paper-trading mode across 190 closed trades over a 15-day period, the system achieves a 54% win rate with net-positive returns (mean +3.10% per trade, +589.3% cumulative), driven by an asymmetric risk/reward profile (skewness 1.86, kurtosis 6.89). We identify statistically significant time-of-day effects: three empirically identified UTC hours exhibit mean P&L of-14.67% versus +4.78% for all other hours (Mann-Whitney U=540.5, p=0.006, rank-biserial r=0.78). A novel counterfactual tracking system monitoring 4,874 rejected token outcomes confirms filter correctness: 54.8% of rejected tokens lost more than 50% within 24 hours, with median returns of-63.8%. We further compare two concurrent architectures-a 13-layer intelligence stack versus a streamlined variant-on identical market data, demonstrating the precision-recall tradeoff inherent in autonomous trading system design.

Economic news releases-including Non-Farm Payrolls, Federal Open Market Committee interest rate decisions, and Consumer Price Index announcements-produce the largest and most predictable short-term price displacements in the retail foreign exchange market. This paper examines the technical architecture of automated news trading systems capable of exploiting these displacements at the retail level. We analyze three critical components: machine-readable data feed infrastructure, pre-hedging execution mechanics, and slippage utilization algorithms. Empirical observations from live trading environments demonstrate that automated systems consistently execute within 5-15 milliseconds of official data release, compared to 500-800 milliseconds for manual execution-a structural advantage that cannot be overcome through skill or experience. We further examine broker selection criteria specific to news trading, indicator filtering methodologies for reducing low-probability setups, and the dual-account architecture that enables systematic performance comparison across execution environments. The analysis draws on 25 years of practical experience developing and operating retail arbitrage and news trading software.

We present AETERNUS, a six-module end-to-end synthetic financial market
pipeline that generates limit order book microstructure, extracts volatility
regimes, compresses correlation tensors, maps systemic risk networks, fore‐
casts return direction with transformers, and runs multi-agent reinforcement
learning tournaments. The pipeline is first validated on synthetic Hestongenerated data, where modules correctly return null results on data contain‐
ing no exploitable structure. It is then applied to 13,652 bars of real ES/NQ/
YM hourly futures data processed through the SRFM (Special Relativistic
Financial Modeling) physics engine. Four pre-registered hypotheses are
tested against the synthetic control. Two are confirmed at extreme signific‐
ance: the Granger causality network forms statistically distinct structure
during SRFM convergence events versus calm periods (t = -11.51, p <
0.0001), and correlation tensor compression improves 50x on real versus
synthetic data. A third hypothesis is supported: the Lumina transformer
achieves 53.0% directional accuracy during convergence windows versus
50.0% on synthetic control, with ablation confirming the contribution of
black hole physics features. The fourth hypothesis (agent Sharpe improve‐
ment) is correctly not supported, with agent rankings inverting in a manner
consistent with the structural differences between synthetic and real data.
To our knowledge, no prior work integrates LOB simulation, volatility sur‐
face modeling, tensor correlation compression, dynamic network topology,
transformer forecasting, and multi-agent competition in a single pipeline
with synthetic-versus-real controlled validation.
KEYWORDS: SYNTHETIC MARKET SIMULATION • STOCHASTIC VOLATILITY • TENSOR DECOMPOSITION •
GRANGER CAUSALITY • TRANSFORMER FORECASTING • MULTI-AGENT REINFORCEMENT LEARNING •
CONTROLLED EXPERIMENT • FINANCIAL PHYSICS

This monograph presents the complete record of Project Event Horizon, a seven-phase experimental research program that develops a unified framework for detecting, measuring, and trading financial market phase transitions. The program draws on techniques from algebraic topology, causal inference, stochastic optimal control, multifractal analysis, self-exciting point processes, deep reinforcement learning, and network graph theory. Beginning with persistent homology and causal DAG discovery in Phase I, the program advances through Student-T hidden Markov models and Ricci curvature-based singularity detection (Phase II), Hamilton-Jacobi-Bellman optimal stopping combined with extreme value theory (Phase III), multifractal detrended fluctuation analysis paired with cross-layer transfer entropy (Phase IV), Hawkes self-exciting processes and Granger causality network collapse (Phase V), on-chain DeFi oracle signals with Bayesian agentic debate (Phase VI), and culminates in a Grand Unified Model that integrates fifteen distinct signals into a topological risk graph with PageRank-based Black Swan Node identification (Phase VII). The Grand Unified Agent achieves an annualized Sharpe ratio of 2.362, representing a 2.5x improvement over the Phase IV baseline. The Singularity Score, a composite measure derived from Hawkes intensity and Ricci curvature, predicts market phase transitions with 88 percent precision at a 20-bar horizon. All code is Python-native. All 80 experimental figures are reproduced in this volume. The program was conducted entirely on synthetic market data calibrated to realistic microstructure dynamics and is presented as a proof-of-concept for topology-aware, multi-domain, agentic market intelligence.

Foreign exchange trading is characterized by high uncertainty, nonlinear price movements, and asymmetric risk exposure, posing persistent challenges for institutional decision-makers. This study develops and evaluates a probability-driven decision support framework grounded in binomial distribution theory to examine how probabilistic modeling of winning trade occurrences relates to perceived profit outcomes and decision quality among professional foreign exchange practitioners. Using a quantitative explanatory research design, cross-sectional survey data were collected from 214 institutional practitioners, including chief investment officers, chief equity strategists, chief investment strategists, forex fund managers, and professional traders employed in Philippine banks, securities firms, and financial institutions. The proposed framework models trading outcomes as binary events and estimates winning trade probabilities using binomial probability principles to support disciplined decision-making and risk calibration. Multiple regression analysis indicates that the probability-driven decision support approach is positively associated with winning trade probability assessment (β = 0.61, p < .001), trade execution discipline (β = 0.56, p < .001), and perceived profit outcomes (β = 0.59, p < .001). These findings suggest that institutional practitioners who incorporate probability-based analytical frameworks report greater consistency in trade evaluation and execution decisions. The study contributes to the literature by extending the application of binomial probability modeling to institutional foreign exchange decision support and by providing an interpretable analytical framework that complements existing quantitative trading approaches in emerging market financial institutions.

High-frequency trading (HFT) in retail forex and cryptocurrency markets has undergone a structural transformation between 2020 and 2026. What was once the exclusive domain of institutional participants with proprietary co-location infrastructure has become accessible to professional retail traders through purpose-built execution platforms, lightweight FIX protocol implementations, and commercial colocation services. This paper examines the technical architecture required for competitive retail HFT in 2026, with particular focus on three interdependent components: (1) the EasyFIX protocol as a lightweight implementation of the Financial Information eXchange standard for simultaneous multi-broker connectivity, (2) VPS colocation strategy and its quantifiable impact on round-trip execution latency, and (3) the relationship between execution latency, slippage, and strategy profitability across latency arbitrage, lock arbitrage, and triangular arbitrage strategy types. The paper draws on operational data from the SharpTrader arbitrage platform, developed and maintained by BJF Trading Group since 2000, and used by professional traders across 50+ countries. Findings indicate that colocation-level latency reduction (from ~200ms to <5ms round-trip) is the single most significant determinant of latency arbitrage profitability, while lock and statistical arbitrage strategies exhibit substantially lower latency sensitivity. Implications for retail traders evaluating HFT infrastructure investment are discussed.

Financial markets stand at a critical juncture. The traditional dominance of human judgment is increasingly challenged by algorithmic systems powered by artificial intelligence and machine learning. This analysis examines that evolving relationship-exploring how techniques from gradient boosting to deep reinforcement learning have transformed market dynamics, while considering where human expertise remains irreplaceable. The conclusion is not one of replacement but of integration: the future belongs to hybrid models that combine human intuition with computational power, governed by robust regulatory and risk management frameworks.

Using the Taiwan Stock Exchange Weighted Index from the first trading day in 1975 to the last trading day in 2007, we investigate the predictability of two popular technical rules (variable-length moving average and trading range breakout) in the Taiwan stock market and assess its bearing on market efficiency. Our results show that, for the two rules, returns from buy signals are generally higher than those from sell signals. In addition, they exhibit considerable predictive power over 1975-1985 and 1986-1996 but become less effective over 1997-2007. These results suggest that the financial reform and liberalization measures (particularly the QFII system) implemented since the early 1990s have contributed, to a certain extent, to the improved efficiency of the Taiwan stock market.

The integration of advanced deep learning architectures, specifically Transformer models, into anti-money laundering (AML) detection systems has significantly improved the identification of illicit financial flows within complex, large-scale banking networks. However, the inherent opacity of these models raises critical concerns regarding algorithmic fairness and the inadvertent penalization of historically marginalized groups. This research article investigates the phenomenon of protected attribute leakage in Transformer-based AML systems, where ostensibly neutral transaction data acts as a proxy for protected characteristics such as race, gender, nationality, or socioeconomic status. Utilizing a mathematically rigorous, synthetic, large-scale banking network dataset to simulate complex global financial behaviors, this study introduces a comprehensive fairness auditing methodology driven by Shapley Additive Explanations (SHAP). By decomposing the multi-head attention mechanisms of a custom Financial Temporal Graph Transformer, we trace how proxy variables disproportionately influence model predictions. The empirical analysis demonstrates that without explicit fairness constraints, Transformer architectures naturally exploit demographic homophily within transaction graphs, leading to systemic bias and high false-positive rates for specific demographic clusters. Our SHAP-driven framework successfully isolates these proxy relationships, enabling financial institutions to audit their models for disparate impact while preserving high predictive utility. Ultimately, this research provides a vital, reproducible blueprint for developing explainable, equitable, and highly accurate AML systems in the era of deep representation learning.